ArticleJournal of medical Internet research2020
A Framework for Applying Natural Language Processing in Digital Health Interventions.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Capitalizing on natural language processing (NLP) to automate the evaluation of coach implementation fidelity in guided digital cognitive-behavioral therapy (GdCBT).Psychological medicine · 2025Trial
- Predicting Satisfaction With Chat-Counseling at a 24/7 Chat Hotline for the Youth: Natural Language Processing Study.JMIR AI · 2025Article
- Personalized insights into liver disease management: a text mining analysis of online consultation data.Frontiers in public health · 2025Article
- Considering the Role of Human Empathy in AI-Driven Therapy.JMIR mental health · 2024Article
- Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing.NPJ digital medicine · 2024Article
- Investigating Patient Satisfaction Through Web-Based Reviews of Norwegian Dentists: Quantitative Study Using the Meaning Extraction Method.Journal of participatory medicine · 2024Article
- Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions.Journal of healthcare informatics research · 2023Article
- Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis.Frontiers in digital health · 2023Article
- Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department.BMC medical research methodology · 2022Article
- Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians.Frontiers in psychiatry · 2022Article
- 40 years of research on eating disorders in domain-specific journals: Bibliometrics, network analysis, and topic modeling.PloS one · 2022Article
- Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining.International journal of environmental research and public health · 2021Review
- Investigating the Use of Electronic Well-being Diaries Completed Within a Psychoeducation Program for University Students: Longitudinal Text Analysis Study.Journal of medical Internet research · 2021Article
- Natural Language Processing-Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study.JMIR research protocols · 2021Article
- Natural Language Processing as an Emerging Tool to Detect Late-Life Depression.Frontiers in psychiatry · 2021Review
- Play the Pain: A Digital Strategy for Play-Oriented Research and Action.Frontiers in psychiatry · 2021Article
- Leveraging the Power of Nondisruptive Technologies to Optimize Mental Health Treatment: Case Study.JMIR mental health · 2020Article
- Social Reminiscence in Older Adults' Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning.Journal of medical Internet research · 2020Article
- Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review.Journal of medical Internet research · 2020Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
backgroundDigital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making.
objectiveThis study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes.
methodsWe first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model.
resultsWe applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms.
conclusionsThe case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.